Recent advances in prosthetic limbs include the provision of multiple degrees of freedom as well as powered actuators that have the potential to provide substantially greater functionality than the passive devices that existed just a decade ago. Despite these engineering accomplishments, developers still struggle with the issue of how to provide the prosthesis user with methods for coordinating the simultaneous control of all of the joints that are involved with, for example, object manipulation in the upper extremity case, or standing and walking in the lower extremity case. This deficit was first apparent for upper extremity prostheses, which now can provide elbow function, wrist rotation, and hand opening and closing. Today's commercially available, upper-extremity prosthetic controllers make use of the EMG activity (electro-myographic activity generated by muscle contraction) of functional native muscles that are present in the amputee's residual limb. This approach allows for proportional control with minimal execution delay. When the EMG activity used for prosthetic control arises from a pair of antagonistic muscles that would normally move the homologous biological joint (e.g., the biceps and triceps controlling flexion and extension, respectively, of the prosthesis elbow joint), the neurally controlled EMG commands are completely intuitive and thus easy to master.
However, more commonly in practice, the same set of EMG signal sources are used to control additional prosthetic joints, and this requires that the command sources be switched among the assigned joints in a serial manner. The resulting motion for most activities is thus awkward, time consuming, and tedious, since it breaks up any compound arm and hand movement into serial positioning steps, resulting in poor utilization of powered prostheses. In the lower extremity, powered ankle and knee joints are just becoming available to the general population. However, commercial lower-extremity prostheses typically do not utilize EMG as a source of control signals. Artificial sensory and computational systems have been demonstrated to provide some degree of control over ankle and knee flexion and extension for powered leg prostheses (E. C. Martinez-Villalpando and H. M. Herr, “Agonist-antagonist active knee prosthesis: A preliminary study in level-ground walking,” Journal of Rehabilitation Research & Development (JRRD), vol. 46, no. 3, pp. 361-73, 2009; S. Au, J. Weber, and H. M Herr, “Powered Ankle-Foot Prosthesis Improves Walking Metabolic Economy,” IEEE Transactions on Robotics, vol. 25, no. 1, pp. 51-66, 2009; H. M. Herr and A. M. Grabowski, “Bionic ankle-foot prosthesis normalizes walking gait for persons with leg amputation,” Proceedings of the Royal Society B, vol. 279, no. 1728, pp. 457-464, February 2012; E. J. Rouse, L. M. Mooney, E. C. Martinez-Villalpando, and H. M. Herr, “A clutchable series-elastic actuator: design of a robotic knee prosthesis for minimum energy consumption,” Proceedings of the IEEE International Conference on Rehabilitation Robotics, 2013). It is well appreciated, however, that the next generation of devices should provide smooth, simultaneous volitional-neural control over several degrees of freedom, such as the knee, ankle and subtalar joints. In that case, simultaneous control of several degrees of freedom will require multiple sources of independent, reliable, and intuitive control that can best be obtained by interfacing with the amputee's extrinsic neural control.
The efficacy of using an EMG-based neural activity approach for achieving simultaneous control of multiple prosthetic joints has been demonstrated in principle by a technique now referred to as “Targeted Muscle Re-innervation,” or TMR (T. A. Kuiken, G. A. Dumanian, R. D. Lipschutz, L. A. Miller, K. A. Stubblefield, “The use of targeted muscle reinnervation for improved myoelectric prosthesis control in a bilateral shoulder disarticulation amputee, Prosthetics and Orthotics International, vol. 28, pp. 245-53, 2004; T. A. Kuiken, “Targeted reinnervation for improved prosthetic function,” Physical Medicine and Rehabilitation Clinics of North America, vol. 17, no. 1, pp. 1-13, 2006). For transhumeral prosthetic control, for example, TMR utilizes the activity of all four of the arm trunk nerves. As a surgical procedure, each trunk nerve is mobilized from the brachial plexus, and each nerve is anastomosed to a separate division of the pectoralis major muscle of the chest. The nerves grow into and innervate their respective new muscle targets and can independently cause contractions of the respectively innervated pectoral muscle divisions. The four recorded muscle signals can then be assigned to prosthetic elbow, wrist, and hand functions according to the original natural hand control function of each of the translocated nerves. For example, hand closing is controlled by evoked EMG activity from the pectoral muscle division innervated by the median nerve, and hand opening is controlled by EMG activity from the muscle division innervated by the radial nerve. Essentially, the operator's brain performs the coordination of the prosthesis joints when a complex task is performed. Despite the laudable success of the original and ensuing demonstrations, the TMR approach has a few shortcomings; for instance, the native innervation of the pectoral muscle (or other selected host muscle) must be removed so that the normal activation of the host muscle by its native innervation does not interfere with that by the transferred nerves. Having to eliminate the functionality of any native tissue for the greater good is not optimal. There are also some limitations regarding how far away a given nerve can be moved in order to connect it to a suitable muscle target. Finally, the use of surface recorded EMG and contiguous muscle targets can lead to inconsistent signal amplitudes and objectionable channel crosstalk (T. A. Kuiken, M. M. Lowery, and N. S. Stoykov, “The effect of subcutaneous fat on myoelectric signal amplitude and cross-talk,” Prosthetics and Orthotics International, vol. 27, no. 1, pp. 48-54, 2003). This last issue has been addressed by using a large array of recording sites and performing substantial pattern recognition to interpret a user's intended movements unambiguously. Over time, however, it is still necessary to “re-tune” the system, which is a substantial inconvenience.
Therefore, there is a need for a method of reversing motor impairment of a human limb, and of restoring at least partial function of a human limb that overcomes or minimizes the above-referenced problems.